Visualisation Compte Rendu
Geoffrey BOILAY
23 février, 2023
Contexte
Chaque enregistrement correpond à un patient qui a réalisé une ponction lombère (LCR). Une LCR n’étant pas réalisée pour des raisons mineurs, tous les patients de la BDD ont présentés des problèmes cognitifs.
Objectif
L’objectif de l’étude est d’analyser l’association entre différents biomarqueurs sanguins et les protéines Abeta et tau, marqueurs de la maladie d’Alzheimer.
Analyses Univariées
Dimension de la BDD
## [1] "Nombre d'observations : 1723"## [1] "Nombre de variables : 176"Variables socio-démographique
Age
Summary
Boxplot
Histogramme
## $breaks
## [1] 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
##
## $counts
## [1] 1 0 1 2 3 12 38 104 163 226 369 335 272 155 40 2
##
## $density
## [1] 0.0001160766 0.0000000000 0.0001160766 0.0002321532 0.0003482298
## [6] 0.0013929193 0.0044109112 0.0120719675 0.0189204875 0.0262333140
## [11] 0.0428322693 0.0388856645 0.0315728381 0.0179918746 0.0046430644
## [16] 0.0002321532
##
## $mids
## [1] 17.5 22.5 27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5
## [16] 92.5
##
## $xname
## [1] "d"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"Sexe
Distribution
Niveau d’étude
Légende
1 = non scolarisé
2 = primaire sans diplôme
3 = certificat d’études
4 = secondaire (collège/CAP/brevet)
5 = secondaire (BEP/lycée)
6 = baccalauréat
7 = études supérieures
8 = non renseigné
Modalités normales
Recodage des Modalités
Barplot
Statut Marital
Distribution
Barplot
## [1] "Jamais marié" "Divorcé" "Marié / en couple"
## [4] "Veuf"
Nombre d’enfants
Modalités normales
Recodage des Modalités
Barplot
Mode de vie
Distribution
Barplot
Comportement de santé
Tabac
Distribution
Barplot
Alcool
Distribution
Barplot
Variables de santé
IMC
Summary
Boxplot
Histogramme
Découpage
Barplot
## [1] "Insuffisance pondérale" "Corpulence normale"
## [3] "Obésité modérée" "Obésité morbide ou massive"
## [5] "Obésité sévère" "Surpoids"
Fréquence Cardiaque
Summary
Boxplot
Histogramme
Distribution
Tension Artérielle Systolique
Summary
Boxplot
Histogramme
Tension Artérielle Diastolique
Summary
Boxplot
Histogramme
Hyper Tension Artérielle
Distribution
Barplot
Dyslipidemie
Distribution
Barplot
Les traitements
Table médicament restant
data2 = clean_names(data2)
L = c("trt1","trt2","trt3","trt4","trt5","trt6","trt7","trt8","trt9","trt10",
"trt11","trt12","trt13","trt14","trt15","trt16","trt17","trt18","trt19","trt20")
trt_total = data.frame(x = data2$trt1)
for (i in 2:length(L)){
b = data.frame(x = data2[,L[i]])
trt_total = rbind(trt_total,b)
}
trt_total = trt_total %>%
mutate(x = tolower(x))%>%
filter(x != c(""))
### Algo
for (i in 1:nrow(trt_total)){
trt_total[i,1] = gsub("y","i", trt_total[i,1])
trt_total[i,1] = gsub("ll","l", trt_total[i,1])
trt_total[i,1] = gsub("ff","f", trt_total[i,1])
trt_total[i,1] = gsub("ph","f", trt_total[i,1])
trt_total[i,1] = gsub("h","", trt_total[i,1])
trt_total[i,1] = gsub("pp","p", trt_total[i,1])
trt_total[i,1] = gsub(",","", trt_total[i,1])
trt_total[i,1] = gsub("_","", trt_total[i,1])
#trt_total[i,1] = gsub(".","", trt_total[i,1])
trt_total[i,1] = gsub("0","", trt_total[i,1])
trt_total[i,1] = gsub("1","", trt_total[i,1])
trt_total[i,1] = gsub("2","", trt_total[i,1])
trt_total[i,1] = gsub("3","", trt_total[i,1])
trt_total[i,1] = gsub("4","", trt_total[i,1])
trt_total[i,1] = gsub("5","", trt_total[i,1])
trt_total[i,1] = gsub("6","", trt_total[i,1])
trt_total[i,1] = gsub("7","", trt_total[i,1])
trt_total[i,1] = gsub("8","", trt_total[i,1])
trt_total[i,1] = gsub("9","", trt_total[i,1])
if (length(grep(" ", trt_total[i,1], ignore.case = TRUE)) > 0){
pos = regexpr(pattern=" ",trt_total[i,1],fixed=TRUE)[1]
trt_total[i,1] = substr(trt_total[i,1],1,pos-1)
}
trt_total[i,1] = gsub(" ","", trt_total[i,1])
if(substr(trt_total[i,1],nchar(trt_total[i,1]),nchar(trt_total[i,1])) == "e"){
trt_total[i,1] = gsub('.{1}$', '', trt_total[i,1])
}
}
medi_total = c(medi_chol,medi_diabete,medi_HTA,medi_antidouleur,medi_anxiete,medi_cv,medi_depression,medi_ma,medi_parkinson,medi_sommeil,medi_trouble,medi_vitamines)
trt_total = trt_total %>%
mutate(x = tolower(x))%>%
filter(x != c(""),!x %in% medi_total)
dat_test <- data.frame(x = trt_total)
med_nb = dat_test %>%
mutate(x = tolower(x)) %>%
filter(dat_test != "",!x %in% medi_total)%>%
gather(value = "Modalités") %>%
group_by(Modalités) %>%
summarise(n = n()) %>% arrange(desc(n))
med_nb = as.data.frame(med_nb)
med_nb#write_xlsx(med_nb,"//172.27.137.244/g_boilay/alternance/export/med.xlsx")
#write.csv(med_nb,"//172.27.137.244/g_boilay/alternance/export/med.csv")MMSE
Summary
Boxplot
Histogramme
Découpage
Barplot
APOE
Distribution
Distribution présence E4
Barplot
Diagnostique clinique
Légende
1 = MA
2 = MCI amnésique
3 = MCI autre
4= Démence mixte
5 = Démence vasculaire
6 = DFT
7 = Lewy
8 = Atrophie corticale postérieure
9 = Démence alcoolique
10 = Aphasie progressive primaire
11 = Trouble psychiatrique
12 = VIH
13 = NSP
14 = Autre
Distribution
Barplot
Biomarqueur de la MA
Abeta42
Distribution
Barplot
Standardisation
Boxplot
Histogramme
Tau
Distribution
Barplot
Standardisation
Boxplot
Histogramme
Biomarqueur sanguin
Cholesterol Total (g/l)
Summary
Boxplot
Histogramme
Découpage
Cholesterol hdl (g/l)
Summary
Boxplot
Histogramme
Découpage
Cholesterol ldl (g/l)
Summary
Boxplot
Histogramme
Glucose à jeun (mmol/l)
Summary
Boxplot
Histogramme
Découpage
Triglycerides (g/l)
Summary
Boxplot
Histogramme
Autres biomarqueurs
Folates (nmol/l)
Summary
Boxplot
Histogramme
Découpage
Vitamine B12 (pmol/l)
Summary
Boxplot
Histogramme
Histogramme
Découpage
Vitamine D (nmol/l)
Summary
Boxplot
Histogramme
Découpage
Proteines (g/l)
Summary
Boxplot
Histogramme
Découpage
TSH (mu/l)
Summary
Boxplot
Histogramme
Découpage
Uree (mmol/l)
Summary
Boxplot
Histogramme
Découpage
Creatinine (umol/l)
Summary
Boxplot
Histogramme
Découpage
Calcium (mmol/l)
Summary
Boxplot
Histogramme
Découpage
## integer(0)Études d’association
Les variables que nous cherchons à expliquer sont l’abeta42 et le tau.
Mise en forme d’abeta42 en modalité
Mise en forme de tau en modalité
Matrice de corrélation
Abeta42 en catégorie
Nombre d’observations dans les modèles
## [1] "Modèle"## [1] " + abeta42t2"## [1] "N = 1723"## [1] " + glucose_jeun"## [1] "N = 1502"## [1] " + triglycerides"## [1] "N = 1282"## [1] " + cholesterol_total"## [1] "N = 1281"## [1] " + age"## [1] "N = 1281"## [1] " + sexe"## [1] "N = 1281"## [1] " + apoe.reg2"## [1] "N = 1177"## [1] " + ch"## [1] "N = 1177"## [1] " + mmse"## [1] "N = 1099"## [1] " + dia"## [1] "N = 1099"## [1] " + imc"## [1] "N = 935"Tableau de fréquence
| abeta42 faible (N=484) | abeta42 normal (N=451) | Total (N=935) | p value | |
|---|---|---|---|---|
| Age | < 0.001 | |||
| - Mean (SD) | 71.145 (8.236) | 67.945 (9.425) | 69.601 (8.968) | |
| - Range | 47.000 - 89.000 | 42.000 - 89.000 | 42.000 - 89.000 | |
| Sexe | 0.015 | |||
| - Femme | 280 (57.9%) | 225 (49.9%) | 505 (54.0%) | |
| - Homme | 204 (42.1%) | 226 (50.1%) | 430 (46.0%) | |
| Glucose | 0.793 | |||
| - Mean (SD) | 5.758 (1.608) | 5.788 (1.870) | 5.773 (1.738) | |
| - Range | 0.720 - 18.500 | 0.920 - 24.500 | 0.720 - 24.500 | |
| Triglycerides | 0.467 | |||
| - Mean (SD) | 0.950 (0.343) | 0.967 (0.363) | 0.958 (0.353) | |
| - Range | 0.380 - 2.020 | 0.150 - 2.020 | 0.150 - 2.020 | |
| Cholesterol | 0.006 | |||
| - Mean (SD) | 2.171 (0.514) | 2.079 (0.512) | 2.127 (0.514) | |
| - Range | 1.030 - 6.640 | 0.970 - 5.900 | 0.970 - 6.640 | |
| Cholesterol hdl | 0.143 | |||
| - Mean (SD) | 0.601 (0.168) | 0.585 (0.152) | 0.593 (0.161) | |
| - Range | 0.210 - 1.590 | 0.280 - 1.380 | 0.210 - 1.590 | |
| Cholesterol ldl | < 0.001 | |||
| - Mean (SD) | 1.374 (0.406) | 1.279 (0.383) | 1.328 (0.398) | |
| - Range | 0.360 - 2.970 | 0.380 - 2.630 | 0.360 - 2.970 | |
| Présence E4 | < 0.001 | |||
| - 0 | 192 (39.7%) | 331 (73.4%) | 523 (55.9%) | |
| - 1 | 220 (45.5%) | 113 (25.1%) | 333 (35.6%) | |
| - 2 | 72 (14.9%) | 7 (1.6%) | 79 (8.4%) | |
| MMSE | < 0.001 | |||
| - Mean (SD) | 21.045 (5.517) | 23.900 (4.410) | 22.422 (5.211) | |
| - Range | 4.000 - 30.000 | 6.000 - 30.000 | 4.000 - 30.000 | |
| Traitement cholesterol | 0.310 | |||
| - 0 | 406 (83.9%) | 389 (86.3%) | 795 (85.0%) | |
| - 1 | 78 (16.1%) | 62 (13.7%) | 140 (15.0%) | |
| Traitement Diabete | 0.190 | |||
| - 0 | 454 (93.8%) | 413 (91.6%) | 867 (92.7%) | |
| - 1 | 30 (6.2%) | 38 (8.4%) | 68 (7.3%) | |
| Niveau d’étude | 0.248 | |||
| - N-Miss | 31 | 36 | 67 | |
| - études supérieurs | 169 (37.3%) | 156 (37.6%) | 325 (37.4%) | |
| - niveau études intermédiaires | 152 (33.6%) | 157 (37.8%) | 309 (35.6%) | |
| - niveau études mineurs | 132 (29.1%) | 102 (24.6%) | 234 (27.0%) |
Modèles sur le Glucose
Légende
## [1] "N = 1062"Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC
Modèle 1
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age, family = binomial(logit),
## data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5436 -1.1814 0.9169 1.1308 1.5433
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.500378 0.517826 -4.829 1.37e-06 ***
## glucose_jeun -0.027111 0.034891 -0.777 0.437
## age 0.039116 0.007082 5.523 3.33e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1439.5 on 1059 degrees of freedom
## AIC: 1445.5
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 2
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe, family = binomial(logit),
## data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.608 -1.177 0.871 1.117 1.647
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.212214 0.588858 -5.455 4.90e-08 ***
## glucose_jeun -0.009055 0.035625 -0.254 0.7994
## age 0.040430 0.007133 5.668 1.45e-08 ***
## sexe 0.337535 0.128259 2.632 0.0085 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1432.5 on 1058 degrees of freedom
## AIC: 1440.5
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 3
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3765 -0.9938 0.4435 1.0069 1.9476
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.961609 0.633271 -6.256 3.96e-10 ***
## glucose_jeun -0.024368 0.037950 -0.642 0.521
## age 0.043812 0.007623 5.747 9.08e-09 ***
## sexe 0.326927 0.136957 2.387 0.017 *
## apoe.reg2 1.254773 0.115784 10.837 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1292.8 on 1057 degrees of freedom
## AIC: 1302.8
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 4
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3826 -0.9898 0.4455 1.0034 1.9484
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.97917 0.64172 -6.201 5.62e-10 ***
## glucose_jeun -0.02507 0.04201 -0.597 0.5507
## age 0.04422 0.00773 5.720 1.07e-08 ***
## sexe 0.32720 0.13697 2.389 0.0169 *
## apoe.reg2 1.25621 0.11611 10.819 < 2e-16 ***
## ch -0.06154 0.19064 -0.323 0.7468
## dia 0.02249 0.27725 0.081 0.9354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1292.7 on 1055 degrees of freedom
## AIC: 1306.7
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 5
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3994 -0.9332 0.3529 0.9654 1.9361
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.193250 0.744224 -1.603 0.1089
## glucose_jeun -0.036122 0.042686 -0.846 0.3974
## age 0.043277 0.008012 5.402 6.60e-08 ***
## sexe 0.245278 0.141668 1.731 0.0834 .
## apoe.reg2 1.209507 0.118358 10.219 < 2e-16 ***
## ch -0.042302 0.195932 -0.216 0.8291
## dia 0.042077 0.285132 0.148 0.8827
## mmse -0.111050 0.014546 -7.634 2.27e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1228.1 on 1054 degrees of freedom
## AIC: 1244.1
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 6
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_am_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3836 -0.9303 0.3485 0.9616 2.0464
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.033108 0.866632 0.038 0.96953
## glucose_jeun -0.012802 0.043970 -0.291 0.77093
## age 0.041457 0.008081 5.130 2.90e-07 ***
## sexe 0.223409 0.142388 1.569 0.11664
## apoe.reg2 1.184760 0.119049 9.952 < 2e-16 ***
## ch 0.002593 0.197607 0.013 0.98953
## dia 0.062715 0.287094 0.218 0.82708
## mmse -0.111574 0.014579 -7.653 1.96e-14 ***
## imc -0.047291 0.017031 -2.777 0.00549 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1471.4 on 1061 degrees of freedom
## Residual deviance: 1220.2 on 1053 degrees of freedom
## AIC: 1238.2
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Tableau récapitulatif
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| glucose_jeun | 0.97 | 0.91, 1.04 | 0.4 | 0.99 | 0.92, 1.06 | 0.8 | 0.98 | 0.91, 1.05 | 0.5 | 0.98 | 0.90, 1.06 | 0.6 | 0.96 | 0.89, 1.05 | 0.4 | 0.99 | 0.91, 1.08 | 0.8 |
| age | 1.04 | 1.03, 1.05 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 |
| sexe | 1.40 | 1.09, 1.80 | 0.008 | 1.39 | 1.06, 1.82 | 0.017 | 1.39 | 1.06, 1.82 | 0.017 | 1.28 | 0.97, 1.69 | 0.083 | 1.25 | 0.95, 1.65 | 0.12 | |||
| apoe.reg2 | 3.51 | 2.81, 4.42 | <0.001 | 3.51 | 2.81, 4.43 | <0.001 | 3.35 | 2.67, 4.24 | <0.001 | 3.27 | 2.60, 4.15 | <0.001 | ||||||
| ch | 0.94 | 0.65, 1.37 | 0.7 | 0.96 | 0.65, 1.41 | 0.8 | 1.00 | 0.68, 1.48 | >0.9 | |||||||||
| dia | 1.02 | 0.59, 1.76 | >0.9 | 1.04 | 0.59, 1.82 | 0.9 | 1.06 | 0.60, 1.87 | 0.8 | |||||||||
| mmse | 0.89 | 0.87, 0.92 | <0.001 | 0.89 | 0.87, 0.92 | <0.001 | ||||||||||||
| imc | 0.95 | 0.92, 0.99 | 0.005 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
Modèles sur la Triglycéride
Légende
## [1] "N = 962"Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE +
IMC
Modèle 1
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age, family = binomial(logit),
## data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5592 -1.1922 0.9114 1.1210 1.5407
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.605758 0.556204 -4.685 2.80e-06 ***
## triglycerides -0.070813 0.184389 -0.384 0.701
## age 0.039595 0.007484 5.291 1.22e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1302.6 on 959 degrees of freedom
## AIC: 1308.6
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 2
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe, family = binomial(logit),
## data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6275 -1.1824 0.8834 1.1120 1.5633
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.293534 0.615915 -5.347 8.92e-08 ***
## triglycerides -0.037037 0.185644 -0.200 0.84187
## age 0.041014 0.007544 5.437 5.43e-08 ***
## sexe 0.361829 0.132684 2.727 0.00639 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1295.1 on 958 degrees of freedom
## AIC: 1303.1
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 3
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4287 -0.9772 0.4346 0.9763 1.8920
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.226190 0.669437 -6.313 2.74e-10 ***
## triglycerides -0.036934 0.200268 -0.184 0.8537
## age 0.045165 0.008122 5.561 2.68e-08 ***
## sexe 0.348478 0.142805 2.440 0.0147 *
## apoe.reg2 1.330147 0.123662 10.756 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1155.6 on 957 degrees of freedom
## AIC: 1165.6
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 4
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4330 -0.9757 0.4301 0.9785 1.8863
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.210505 0.671931 -6.266 3.70e-10 ***
## triglycerides -0.025122 0.201053 -0.125 0.9006
## age 0.045092 0.008236 5.475 4.38e-08 ***
## sexe 0.339107 0.143498 2.363 0.0181 *
## apoe.reg2 1.328357 0.123814 10.729 < 2e-16 ***
## ch 0.054458 0.207306 0.263 0.7928
## dia -0.189006 0.273658 -0.691 0.4898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1155.1 on 955 degrees of freedom
## AIC: 1169.1
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 5
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4098 -0.9194 0.3757 0.9464 1.9758
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.516928 0.780623 -1.943 0.0520 .
## triglycerides -0.101392 0.205252 -0.494 0.6213
## age 0.044231 0.008526 5.188 2.13e-07 ***
## sexe 0.276219 0.147843 1.868 0.0617 .
## apoe.reg2 1.258855 0.125675 10.017 < 2e-16 ***
## ch 0.068487 0.212689 0.322 0.7474
## dia -0.199196 0.282210 -0.706 0.4803
## mmse -0.107754 0.015390 -7.001 2.53e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1101.1 on 954 degrees of freedom
## AIC: 1117.1
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 6
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_am_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4214 -0.9213 0.3779 0.9471 2.0710
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.400336 0.906199 -0.442 0.6587
## triglycerides -0.023539 0.208083 -0.113 0.9099
## age 0.043332 0.008592 5.043 4.58e-07 ***
## sexe 0.245164 0.148821 1.647 0.0995 .
## apoe.reg2 1.241490 0.126370 9.824 < 2e-16 ***
## ch 0.107645 0.214328 0.502 0.6155
## dia -0.133611 0.284301 -0.470 0.6384
## mmse -0.107439 0.015392 -6.980 2.95e-12 ***
## imc -0.043708 0.017980 -2.431 0.0151 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1332.0 on 961 degrees of freedom
## Residual deviance: 1095.1 on 953 degrees of freedom
## AIC: 1113.1
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Tableau récapitulatif
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| triglycerides | 0.93 | 0.65, 1.34 | 0.7 | 0.96 | 0.67, 1.39 | 0.8 | 0.96 | 0.65, 1.43 | 0.9 | 0.98 | 0.66, 1.45 | >0.9 | 0.90 | 0.60, 1.35 | 0.6 | 0.98 | 0.65, 1.47 | >0.9 |
| age | 1.04 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 |
| sexe | 1.44 | 1.11, 1.86 | 0.006 | 1.42 | 1.07, 1.88 | 0.015 | 1.40 | 1.06, 1.86 | 0.018 | 1.32 | 0.99, 1.76 | 0.062 | 1.28 | 0.95, 1.71 | 0.10 | |||
| apoe.reg2 | 3.78 | 2.98, 4.84 | <0.001 | 3.77 | 2.97, 4.83 | <0.001 | 3.52 | 2.76, 4.53 | <0.001 | 3.46 | 2.71, 4.45 | <0.001 | ||||||
| ch | 1.06 | 0.70, 1.59 | 0.8 | 1.07 | 0.71, 1.63 | 0.7 | 1.11 | 0.73, 1.70 | 0.6 | |||||||||
| dia | 0.83 | 0.48, 1.41 | 0.5 | 0.82 | 0.47, 1.42 | 0.5 | 0.87 | 0.50, 1.53 | 0.6 | |||||||||
| mmse | 0.90 | 0.87, 0.92 | <0.001 | 0.90 | 0.87, 0.93 | <0.001 | ||||||||||||
| imc | 0.96 | 0.92, 0.99 | 0.015 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
Modèles sur le Cholestérol
Légende
## [1] "N = 1006"Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE +
IMC
Modèle 1
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age, family = binomial(logit),
## data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9446 -1.1586 0.8581 1.1197 1.5276
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.723353 0.626945 -5.939 2.87e-09 ***
## cholesterol_total 0.393784 0.131439 2.996 0.00274 **
## age 0.042260 0.007404 5.708 1.14e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1354.4 on 1003 degrees of freedom
## AIC: 1360.4
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 2
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe, family = binomial(logit),
## data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.853 -1.164 0.839 1.123 1.543
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.996785 0.642066 -6.225 4.82e-10 ***
## cholesterol_total 0.302169 0.137823 2.192 0.0283 *
## age 0.042807 0.007427 5.763 8.25e-09 ***
## sexe 0.282006 0.136797 2.061 0.0393 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1350.1 on 1002 degrees of freedom
## AIC: 1358.1
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 3
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4789 -0.9833 0.4258 1.0000 1.8767
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.688035 0.688074 -6.813 9.54e-12 ***
## cholesterol_total 0.188765 0.142824 1.322 0.1863
## age 0.046406 0.007952 5.835 5.36e-09 ***
## sexe 0.308945 0.146143 2.114 0.0345 *
## apoe.reg2 1.279867 0.120011 10.665 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1215.2 on 1001 degrees of freedom
## AIC: 1225.2
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 4
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4799 -0.9833 0.4254 1.0008 1.8782
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.699349 0.691618 -6.795 1.09e-11 ***
## cholesterol_total 0.190582 0.144377 1.320 0.1868
## age 0.046448 0.008042 5.775 7.68e-09 ***
## sexe 0.310646 0.146485 2.121 0.0339 *
## apoe.reg2 1.280625 0.120094 10.664 < 2e-16 ***
## ch -0.011952 0.199579 -0.060 0.9522
## dia 0.043510 0.262723 0.166 0.8685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1215.2 on 999 degrees of freedom
## AIC: 1229.2
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 5
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5070 -0.9507 0.3601 0.9486 2.0180
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.204502 0.779108 -2.830 0.00466 **
## cholesterol_total 0.249671 0.148804 1.678 0.09338 .
## age 0.046152 0.008331 5.540 3.02e-08 ***
## sexe 0.224276 0.151445 1.481 0.13863
## apoe.reg2 1.208232 0.122048 9.900 < 2e-16 ***
## ch 0.018260 0.204781 0.089 0.92895
## dia 0.034869 0.270726 0.129 0.89752
## mmse -0.107932 0.015054 -7.170 7.53e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1158.6 on 998 degrees of freedom
## AIC: 1174.6
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Modèle 6
Modèle
##
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_am_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4778 -0.9344 0.3494 0.9521 2.0426
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.065190 0.930610 -1.145 0.2524
## cholesterol_total 0.222673 0.149946 1.485 0.1375
## age 0.044793 0.008394 5.336 9.48e-08 ***
## sexe 0.201153 0.152256 1.321 0.1864
## apoe.reg2 1.191730 0.122564 9.723 < 2e-16 ***
## ch 0.048016 0.205996 0.233 0.8157
## dia 0.090926 0.273085 0.333 0.7392
## mmse -0.107735 0.015061 -7.153 8.48e-13 ***
## imc -0.038209 0.017098 -2.235 0.0254 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1393.8 on 1005 degrees of freedom
## Residual deviance: 1153.6 on 997 degrees of freedom
## AIC: 1171.6
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...Tableau récapitulatif
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| triglycerides | 0.93 | 0.65, 1.34 | 0.7 | 0.96 | 0.67, 1.39 | 0.8 | 0.96 | 0.65, 1.43 | 0.9 | 0.98 | 0.66, 1.45 | >0.9 | 0.90 | 0.60, 1.35 | 0.6 | 0.98 | 0.65, 1.47 | >0.9 |
| age | 1.04 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.05 | 1.03, 1.06 | <0.001 | 1.04 | 1.03, 1.06 | <0.001 |
| sexe | 1.44 | 1.11, 1.86 | 0.006 | 1.42 | 1.07, 1.88 | 0.015 | 1.40 | 1.06, 1.86 | 0.018 | 1.32 | 0.99, 1.76 | 0.062 | 1.28 | 0.95, 1.71 | 0.10 | |||
| apoe.reg2 | 3.78 | 2.98, 4.84 | <0.001 | 3.77 | 2.97, 4.83 | <0.001 | 3.52 | 2.76, 4.53 | <0.001 | 3.46 | 2.71, 4.45 | <0.001 | ||||||
| ch | 1.06 | 0.70, 1.59 | 0.8 | 1.07 | 0.71, 1.63 | 0.7 | 1.11 | 0.73, 1.70 | 0.6 | |||||||||
| dia | 0.83 | 0.48, 1.41 | 0.5 | 0.82 | 0.47, 1.42 | 0.5 | 0.87 | 0.50, 1.53 | 0.6 | |||||||||
| mmse | 0.90 | 0.87, 0.92 | <0.001 | 0.90 | 0.87, 0.93 | <0.001 | ||||||||||||
| imc | 0.96 | 0.92, 0.99 | 0.015 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
Abeta42 en continu
Nombre d’Observations dans les modèles
## [1] "Modèle"## [1] " + abeta42_"## [1] "N = 1721"## [1] " + glucose_jeun"## [1] "N = 1501"## [1] " + triglycerides"## [1] "N = 1281"## [1] " + cholesterol_total"## [1] "N = 1280"## [1] " + age"## [1] "N = 1280"## [1] " + sexe"## [1] "N = 1280"## [1] " + apoe.reg2"## [1] "N = 1176"## [1] " + ch"## [1] "N = 1176"## [1] " + mmse"## [1] "N = 1098"## [1] " + imc"## [1] "N = 934"## [1] " + dia"## [1] "N = 934"Matrice de corrélation
Modèles sur le Glucose
Légende
## [1] "N = 1061"Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC
Modèle 1
Modèle
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2522 -0.7928 -0.2886 0.8518 5.5710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.321998 0.252683 1.274 0.20283
## glucose_jeun 0.048287 0.017553 2.751 0.00605 **
## age -0.008519 0.003435 -2.480 0.01329 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.012 on 1058 degrees of freedom
## Multiple R-squared: 0.01178, Adjusted R-squared: 0.009916
## F-statistic: 6.308 on 2 and 1058 DF, p-value: 0.001891Scatter plot
Régression linéaire
## Warning in abline(abeta42_glucose.mod, col = "red"): utilisation des deux
## premiers des 3 coefficients de régression
Modèle 2
Modèle
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2430 -0.7944 -0.2981 0.8569 5.5714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.359078 0.284562 1.262 0.20728
## glucose_jeun 0.047319 0.017889 2.645 0.00829 **
## age -0.008575 0.003442 -2.491 0.01289 *
## sexe -0.018057 0.063628 -0.284 0.77663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.013 on 1057 degrees of freedom
## Multiple R-squared: 0.01186, Adjusted R-squared: 0.009055
## F-statistic: 4.229 on 3 and 1057 DF, p-value: 0.005545confint(mod, 'sexe', level=0.95)## 2.5 % 97.5 %
## sexe -0.1429078 0.1067938summary(mod)$coefficients## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.359077530 0.284562175 1.2618597 0.207277663
## glucose_jeun 0.047318930 0.017889112 2.6451246 0.008287209
## age -0.008574568 0.003442012 -2.4911500 0.012885544
## sexe -0.018056997 0.063627600 -0.2837919 0.776625443Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
Modèle 3
Modèle
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2,
## data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2073 -0.7938 -0.3108 0.8399 5.5089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.394297 0.284458 1.386 0.16600
## glucose_jeun 0.048223 0.017860 2.700 0.00704 **
## age -0.008456 0.003436 -2.461 0.01401 *
## sexe -0.013350 0.063542 -0.210 0.83363
## apoe.reg2 -0.107640 0.048069 -2.239 0.02535 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1056 degrees of freedom
## Multiple R-squared: 0.01653, Adjusted R-squared: 0.0128
## F-statistic: 4.437 on 4 and 1056 DF, p-value: 0.001466Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
Modèle 4
Modèle
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3452 -0.7965 -0.2960 0.8373 5.4255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.366298 0.288365 1.270 0.20427
## glucose_jeun 0.055361 0.019554 2.831 0.00473 **
## age -0.008514 0.003486 -2.442 0.01476 *
## sexe -0.013709 0.063578 -0.216 0.82933
## apoe.reg2 -0.111397 0.048299 -2.306 0.02128 *
## ch 0.014411 0.088269 0.163 0.87034
## dia -0.119165 0.130848 -0.911 0.36266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1054 degrees of freedom
## Multiple R-squared: 0.0173, Adjusted R-squared: 0.01171
## F-statistic: 3.093 on 6 and 1054 DF, p-value: 0.005237Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
Modèle 5
Modèle
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3449 -0.8016 -0.2889 0.8364 5.4073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.170117 0.331867 0.513 0.60834
## glucose_jeun 0.056086 0.019560 2.867 0.00422 **
## age -0.008275 0.003492 -2.370 0.01797 *
## sexe -0.007248 0.063796 -0.114 0.90956
## apoe.reg2 -0.103763 0.048711 -2.130 0.03339 *
## ch 0.011375 0.088288 0.129 0.89751
## dia -0.120472 0.130827 -0.921 0.35734
## mmse 0.007206 0.006037 1.194 0.23291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1053 degrees of freedom
## Multiple R-squared: 0.01863, Adjusted R-squared: 0.01211
## F-statistic: 2.856 on 7 and 1053 DF, p-value: 0.005901Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
Modèle 6
Modèle
##
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_ac_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3739 -0.8026 -0.2823 0.8396 5.3762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.268034 0.388588 0.690 0.49049
## glucose_jeun 0.058026 0.019972 2.905 0.00374 **
## age -0.008446 0.003511 -2.406 0.01631 *
## sexe -0.009209 0.063947 -0.144 0.88552
## apoe.reg2 -0.106274 0.049003 -2.169 0.03033 *
## ch 0.014225 0.088516 0.161 0.87236
## dia -0.118092 0.130966 -0.902 0.36742
## mmse 0.007172 0.006040 1.187 0.23532
## imc -0.003708 0.007648 -0.485 0.62788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1052 degrees of freedom
## Multiple R-squared: 0.01885, Adjusted R-squared: 0.01139
## F-statistic: 2.526 on 8 and 1052 DF, p-value: 0.01005Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
Tableau récapitulatif
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | 0.01 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 |
| [-0.05, 0.07],(0.74) | [-0.07, 0.11],(0.66) | [-0.07, 0.11],(0.70) | [-0.07, 0.12],(0.61) | [-0.07, 0.12],(0.66) | [-0.07, 0.12],(0.65) | |
| glucose_jeun | 0.09 ** | 0.08 ** | 0.09 ** | 0.10 ** | 0.10 ** | 0.10 ** |
| [0.02, 0.15],(0.01) | [0.02, 0.15],(0.01) | [0.02, 0.15],(0.01) | [0.03, 0.17],(0.00) | [0.03, 0.17],(0.00) | [0.03, 0.17],(0.00) | |
| age | -0.08 * | -0.08 * | -0.08 * | -0.08 * | -0.08 * | -0.08 * |
| [-0.14, -0.02],(0.01) | [-0.14, -0.02],(0.01) | [-0.14, -0.02],(0.01) | [-0.14, -0.02],(0.01) | [-0.14, -0.01],(0.02) | [-0.14, -0.01],(0.02) | |
| sexe | -0.02 | -0.01 | -0.01 | -0.01 | -0.01 | |
| [-0.14, 0.11],(0.78) | [-0.14, 0.11],(0.83) | [-0.14, 0.11],(0.83) | [-0.13, 0.12],(0.91) | [-0.13, 0.12],(0.89) | ||
| apoe.reg2 | -0.07 * | -0.07 * | -0.07 * | -0.07 * | ||
| [-0.13, -0.01],(0.03) | [-0.13, -0.01],(0.02) | [-0.13, -0.01],(0.03) | [-0.13, -0.01],(0.03) | |||
| ch | 0.01 | 0.01 | 0.01 | |||
| [-0.16, 0.19],(0.87) | [-0.16, 0.18],(0.90) | [-0.16, 0.19],(0.87) | ||||
| dia | -0.12 | -0.12 | -0.12 | |||
| [-0.38, 0.14],(0.36) | [-0.38, 0.14],(0.36) | [-0.38, 0.14],(0.37) | ||||
| mmse | 0.04 | 0.04 | ||||
| [-0.02, 0.10],(0.23) | [-0.02, 0.10],(0.24) | |||||
| imc | -0.02 | |||||
| [-0.08, 0.05],(0.63) | ||||||
| N | 1061 | 1061 | 1061 | 1061 | 1061 | 1061 |
| R2 | 0.01 | 0.01 | 0.02 | 0.02 | 0.02 | 0.02 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
Modèles sur la Triglycéride
Légende
## [1] "N = 961"Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE +
IMC
Modèle 1
Modèle
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1979 -0.8162 -0.2727 0.8537 5.9907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.690215 0.273350 2.525 0.0117 *
## triglycerides -0.144098 0.092347 -1.560 0.1190
## age -0.007442 0.003659 -2.034 0.0422 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 958 degrees of freedom
## Multiple R-squared: 0.006705, Adjusted R-squared: 0.004631
## F-statistic: 3.233 on 2 and 958 DF, p-value: 0.03985Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression
Modèle 2
Modèle
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2404 -0.8152 -0.2611 0.8476 5.9443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.835103 0.299127 2.792 0.00535 **
## triglycerides -0.151771 0.092551 -1.640 0.10136
## age -0.007674 0.003663 -2.095 0.03644 *
## sexe -0.078787 0.066129 -1.191 0.23379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 957 degrees of freedom
## Multiple R-squared: 0.008176, Adjusted R-squared: 0.005067
## F-statistic: 2.63 on 3 and 957 DF, p-value: 0.04898Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
Modèle 3
Modèle
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2,
## data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2866 -0.7963 -0.2712 0.8428 5.8990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.870179 0.299414 2.906 0.00374 **
## triglycerides -0.150982 0.092444 -1.633 0.10275
## age -0.007597 0.003659 -2.076 0.03816 *
## sexe -0.074218 0.066101 -1.123 0.26180
## apoe.reg2 -0.091341 0.050781 -1.799 0.07238 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.017 on 956 degrees of freedom
## Multiple R-squared: 0.01152, Adjusted R-squared: 0.007386
## F-statistic: 2.786 on 4 and 956 DF, p-value: 0.02557Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
Modèle 4
Modèle
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2788 -0.8147 -0.2696 0.8463 5.9044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.879069 0.300994 2.921 0.00358 **
## triglycerides -0.151329 0.092869 -1.629 0.10354
## age -0.007820 0.003717 -2.104 0.03563 *
## sexe -0.073304 0.066433 -1.103 0.27012
## apoe.reg2 -0.091221 0.050905 -1.792 0.07345 .
## ch 0.030683 0.095480 0.321 0.74801
## dia 0.014427 0.130298 0.111 0.91186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 954 degrees of freedom
## Multiple R-squared: 0.01166, Adjusted R-squared: 0.005441
## F-statistic: 1.875 on 6 and 954 DF, p-value: 0.08211Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
Modèle 5
Modèle
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2877 -0.8085 -0.2659 0.8394 5.9002
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.791314 0.347122 2.280 0.0228 *
## triglycerides -0.149548 0.092971 -1.609 0.1080
## age -0.007714 0.003724 -2.072 0.0386 *
## sexe -0.070977 0.066616 -1.065 0.2869
## apoe.reg2 -0.087190 0.051539 -1.692 0.0910 .
## ch 0.029565 0.095542 0.309 0.7571
## dia 0.014487 0.130349 0.111 0.9115
## mmse 0.003262 0.006420 0.508 0.6115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 953 degrees of freedom
## Multiple R-squared: 0.01192, Adjusted R-squared: 0.004667
## F-statistic: 1.643 on 7 and 953 DF, p-value: 0.1197Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
Modèle 6
Modèle
##
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_ac_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2904 -0.8051 -0.2551 0.8408 5.8910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.909149 0.408958 2.223 0.0264 *
## triglycerides -0.141928 0.094049 -1.509 0.1316
## age -0.007864 0.003735 -2.105 0.0355 *
## sexe -0.074296 0.066918 -1.110 0.2672
## apoe.reg2 -0.089830 0.051784 -1.735 0.0831 .
## ch 0.032643 0.095744 0.341 0.7332
## dia 0.022714 0.131267 0.173 0.8627
## mmse 0.003270 0.006422 0.509 0.6108
## imc -0.004398 0.008062 -0.545 0.5855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 952 degrees of freedom
## Multiple R-squared: 0.01223, Adjusted R-squared: 0.003933
## F-statistic: 1.474 on 8 and 952 DF, p-value: 0.1625Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
Tableau récapitulatif
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | 0.03 | 0.08 | 0.07 | 0.07 | 0.07 | 0.07 |
| [-0.03, 0.10],(0.31) | [-0.02, 0.17],(0.12) | [-0.02, 0.17],(0.13) | [-0.03, 0.17],(0.19) | [-0.04, 0.17],(0.20) | [-0.03, 0.17],(0.20) | |
| triglycerides | -0.05 | -0.05 | -0.05 | -0.05 | -0.05 | -0.05 |
| [-0.12, 0.01],(0.12) | [-0.12, 0.01],(0.10) | [-0.12, 0.01],(0.10) | [-0.12, 0.01],(0.10) | [-0.12, 0.01],(0.11) | [-0.12, 0.02],(0.13) | |
| age | -0.07 * | -0.07 * | -0.07 * | -0.07 * | -0.07 * | -0.07 * |
| [-0.13, -0.00],(0.04) | [-0.13, -0.00],(0.04) | [-0.13, -0.00],(0.04) | [-0.14, -0.00],(0.04) | [-0.13, -0.00],(0.04) | [-0.14, -0.00],(0.04) | |
| sexe | -0.08 | -0.07 | -0.07 | -0.07 | -0.07 | |
| [-0.21, 0.05],(0.23) | [-0.20, 0.06],(0.26) | [-0.20, 0.06],(0.27) | [-0.20, 0.06],(0.29) | [-0.21, 0.06],(0.27) | ||
| apoe.reg2 | -0.06 | -0.06 | -0.06 | -0.06 | ||
| [-0.12, 0.01],(0.07) | [-0.12, 0.01],(0.07) | [-0.12, 0.01],(0.09) | [-0.12, 0.01],(0.08) | |||
| ch | 0.03 | 0.03 | 0.03 | |||
| [-0.16, 0.22],(0.75) | [-0.16, 0.22],(0.76) | [-0.16, 0.22],(0.73) | ||||
| dia | 0.01 | 0.01 | 0.02 | |||
| [-0.24, 0.27],(0.91) | [-0.24, 0.27],(0.91) | [-0.23, 0.28],(0.86) | ||||
| mmse | 0.02 | 0.02 | ||||
| [-0.05, 0.08],(0.61) | [-0.05, 0.08],(0.61) | |||||
| imc | -0.02 | |||||
| [-0.09, 0.05],(0.59) | ||||||
| N | 961 | 961 | 961 | 961 | 961 | 961 |
| R2 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
Modèles sur le Cholestérol
Légende
## [1] "N = 1005"Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE +
IMC
Modèle 1
Modèle
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1742 -0.8281 -0.2635 0.8471 6.0522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.649197 0.299908 2.165 0.0306 *
## cholesterol_total -0.059883 0.062887 -0.952 0.3412
## age -0.007015 0.003599 -1.949 0.0516 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 1002 degrees of freedom
## Multiple R-squared: 0.004292, Adjusted R-squared: 0.002304
## F-statistic: 2.159 on 2 and 1002 DF, p-value: 0.1159Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression
Modèle 2
Modèle
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1985 -0.8230 -0.2632 0.8375 6.0252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.698118 0.307034 2.274 0.0232 *
## cholesterol_total -0.043910 0.066433 -0.661 0.5088
## age -0.007085 0.003601 -1.968 0.0494 *
## sexe -0.051062 0.068329 -0.747 0.4551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 1001 degrees of freedom
## Multiple R-squared: 0.004847, Adjusted R-squared: 0.001865
## F-statistic: 1.625 on 3 and 1001 DF, p-value: 0.1819Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
Modèle 3
Modèle
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2,
## data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2517 -0.8074 -0.2616 0.8583 5.9719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.714809 0.306646 2.331 0.0199 *
## cholesterol_total -0.031217 0.066612 -0.469 0.6394
## age -0.006955 0.003596 -1.934 0.0533 .
## sexe -0.050057 0.068220 -0.734 0.4633
## apoe.reg2 -0.103362 0.050222 -2.058 0.0398 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.02 on 1000 degrees of freedom
## Multiple R-squared: 0.009044, Adjusted R-squared: 0.005081
## F-statistic: 2.282 on 4 and 1000 DF, p-value: 0.05878Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
Modèle 4
Modèle
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2472 -0.8070 -0.2575 0.8599 5.9748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.716796 0.308275 2.325 0.0203 *
## cholesterol_total -0.029337 0.067329 -0.436 0.6631
## age -0.007085 0.003643 -1.945 0.0521 .
## sexe -0.050225 0.068439 -0.734 0.4632
## apoe.reg2 -0.103588 0.050353 -2.057 0.0399 *
## ch 0.020709 0.092994 0.223 0.8238
## dia 0.003516 0.126131 0.028 0.9778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 998 degrees of freedom
## Multiple R-squared: 0.009097, Adjusted R-squared: 0.00314
## F-statistic: 1.527 on 6 and 998 DF, p-value: 0.1659Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
Modèle 5
Modèle
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2618 -0.8055 -0.2617 0.8473 5.9674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.584556 0.347038 1.684 0.0924 .
## cholesterol_total -0.032614 0.067455 -0.483 0.6289
## age -0.006939 0.003648 -1.902 0.0574 .
## sexe -0.045238 0.068713 -0.658 0.5105
## apoe.reg2 -0.096972 0.050987 -1.902 0.0575 .
## ch 0.018139 0.093060 0.195 0.8455
## dia 0.003948 0.126152 0.031 0.9750
## mmse 0.005270 0.006348 0.830 0.4066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 997 degrees of freedom
## Multiple R-squared: 0.009782, Adjusted R-squared: 0.00283
## F-statistic: 1.407 on 7 and 997 DF, p-value: 0.1987Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
Modèle 6
Modèle
##
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_ac_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2681 -0.8062 -0.2525 0.8474 5.9504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.763616 0.420997 1.814 0.0700 .
## cholesterol_total -0.036553 0.067673 -0.540 0.5892
## age -0.007211 0.003666 -1.967 0.0495 *
## sexe -0.049148 0.068924 -0.713 0.4760
## apoe.reg2 -0.100368 0.051198 -1.960 0.0502 .
## ch 0.021676 0.093199 0.233 0.8161
## dia 0.014765 0.126997 0.116 0.9075
## mmse 0.005287 0.006350 0.833 0.4052
## imc -0.005825 0.007749 -0.752 0.4524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 996 degrees of freedom
## Multiple R-squared: 0.01034, Adjusted R-squared: 0.002394
## F-statistic: 1.301 on 8 and 996 DF, p-value: 0.2389Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
Tableau récapitulatif
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | 0.03 | 0.06 | 0.06 | 0.06 | 0.05 | 0.05 |
| [-0.03, 0.10],(0.30) | [-0.03, 0.16],(0.21) | [-0.04, 0.15],(0.22) | [-0.04, 0.16],(0.27) | [-0.05, 0.15],(0.29) | [-0.05, 0.16],(0.28) | |
| cholesterol_total | -0.03 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 |
| [-0.09, 0.03],(0.34) | [-0.09, 0.04],(0.51) | [-0.08, 0.05],(0.64) | [-0.08, 0.05],(0.66) | [-0.09, 0.05],(0.63) | [-0.09, 0.05],(0.59) | |
| age | -0.06 | -0.06 * | -0.06 | -0.06 | -0.06 | -0.07 * |
| [-0.13, 0.00],(0.05) | [-0.13, -0.00],(0.05) | [-0.13, 0.00],(0.05) | [-0.13, 0.00],(0.05) | [-0.13, 0.00],(0.06) | [-0.13, -0.00],(0.05) | |
| sexe | -0.05 | -0.05 | -0.05 | -0.05 | -0.05 | |
| [-0.19, 0.08],(0.46) | [-0.18, 0.08],(0.46) | [-0.18, 0.08],(0.46) | [-0.18, 0.09],(0.51) | [-0.18, 0.09],(0.48) | ||
| apoe.reg2 | -0.07 * | -0.07 * | -0.06 | -0.06 | ||
| [-0.13, -0.00],(0.04) | [-0.13, -0.00],(0.04) | [-0.13, 0.00],(0.06) | [-0.13, 0.00],(0.05) | |||
| ch | 0.02 | 0.02 | 0.02 | |||
| [-0.16, 0.20],(0.82) | [-0.16, 0.20],(0.85) | [-0.16, 0.20],(0.82) | ||||
| dia | 0.00 | 0.00 | 0.01 | |||
| [-0.24, 0.25],(0.98) | [-0.24, 0.25],(0.98) | [-0.23, 0.26],(0.91) | ||||
| mmse | 0.03 | 0.03 | ||||
| [-0.04, 0.09],(0.41) | [-0.04, 0.09],(0.41) | |||||
| imc | -0.02 | |||||
| [-0.09, 0.04],(0.45) | ||||||
| N | 1005 | 1005 | 1005 | 1005 | 1005 | 1005 |
| R2 | 0.00 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
Tau en catégorie
Nombre d’Observations dans les modèles
## [1] "Modèle"## [1] " + taut2"## [1] "N = 1723"## [1] " + glucose_jeun"## [1] "N = 1502"## [1] " + triglycerides"## [1] "N = 1282"## [1] " + cholesterol_total"## [1] "N = 1281"## [1] " + age"## [1] "N = 1281"## [1] " + sexe"## [1] "N = 1281"## [1] " + apoe.reg2"## [1] "N = 1177"## [1] " + ch"## [1] "N = 1177"## [1] " + mmse"## [1] "N = 1099"## [1] " + dia"## [1] "N = 1099"## [1] " + imc"## [1] "N = 935"Tableau de fréquence
| FALSE (N=413) | TRUE (N=522) | Total (N=935) | p value | |
|---|---|---|---|---|
| Age | < 0.001 | |||
| - Mean (SD) | 67.172 (9.292) | 71.523 (8.218) | 69.601 (8.968) | |
| - Range | 42.000 - 88.000 | 49.000 - 89.000 | 42.000 - 89.000 | |
| Sexe | < 0.001 | |||
| - Femme | 196 (47.5%) | 309 (59.2%) | 505 (54.0%) | |
| - Homme | 217 (52.5%) | 213 (40.8%) | 430 (46.0%) | |
| Glucose | 0.761 | |||
| - Mean (SD) | 5.753 (1.721) | 5.788 (1.754) | 5.773 (1.738) | |
| - Range | 0.720 - 24.500 | 0.920 - 20.900 | 0.720 - 24.500 | |
| Triglycerides | 0.017 | |||
| - Mean (SD) | 0.989 (0.364) | 0.934 (0.343) | 0.958 (0.353) | |
| - Range | 0.150 - 2.020 | 0.340 - 2.020 | 0.150 - 2.020 | |
| Cholesterol | 0.066 | |||
| - Mean (SD) | 2.092 (0.516) | 2.154 (0.512) | 2.127 (0.514) | |
| - Range | 0.970 - 5.900 | 1.030 - 6.640 | 0.970 - 6.640 | |
| Cholesterol hdl | < 0.001 | |||
| - Mean (SD) | 0.572 (0.154) | 0.610 (0.165) | 0.593 (0.161) | |
| - Range | 0.220 - 1.590 | 0.210 - 1.100 | 0.210 - 1.590 | |
| Cholesterol ldl | 0.055 | |||
| - Mean (SD) | 1.300 (0.383) | 1.350 (0.408) | 1.328 (0.398) | |
| - Range | 0.480 - 2.970 | 0.360 - 2.730 | 0.360 - 2.970 | |
| Présence E4 | < 0.001 | |||
| - 0 | 291 (70.5%) | 232 (44.4%) | 523 (55.9%) | |
| - 1 | 103 (24.9%) | 230 (44.1%) | 333 (35.6%) | |
| - 2 | 19 (4.6%) | 60 (11.5%) | 79 (8.4%) | |
| MMSE | < 0.001 | |||
| - Mean (SD) | 23.913 (4.453) | 21.243 (5.462) | 22.422 (5.211) | |
| - Range | 8.000 - 30.000 | 4.000 - 30.000 | 4.000 - 30.000 | |
| Traitement cholesterol | 0.734 | |||
| - 0 | 353 (85.5%) | 442 (84.7%) | 795 (85.0%) | |
| - 1 | 60 (14.5%) | 80 (15.3%) | 140 (15.0%) | |
| Traitement Diabete | 0.619 | |||
| - 0 | 381 (92.3%) | 486 (93.1%) | 867 (92.7%) | |
| - 1 | 32 (7.7%) | 36 (6.9%) | 68 (7.3%) | |
| Niveau d’étude | 0.466 | |||
| - N-Miss | 29 | 38 | 67 | |
| - études supérieurs | 141 (36.7%) | 184 (38.0%) | 325 (37.4%) | |
| - niveau études intermédiaires | 145 (37.8%) | 164 (33.9%) | 309 (35.6%) | |
| - niveau études mineurs | 98 (25.5%) | 136 (28.1%) | 234 (27.0%) |
Modèles sur le Glucose
Légende
## [1] "N = 1062"Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC
Modèle 1
Modèle
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age, family = binomial(logit),
## data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7760 -1.1958 0.8034 1.0600 1.6739
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.675284 0.538412 -6.826 8.72e-12 ***
## glucose_jeun -0.036869 0.035453 -1.040 0.298
## age 0.059360 0.007439 7.980 1.47e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1390.0 on 1059 degrees of freedom
## AIC: 1396
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0253422 0.0087008 0.0719 8.722e-12 ***
## glucose_jeun 0.9638019 0.8989036 1.0341 0.2984
## age 1.0611570 1.0459901 1.0770 1.468e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 2
Modèle
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe, family = binomial(logit),
## data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9032 -1.1755 0.7584 1.0404 1.8355
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.802182 0.621516 -7.727 1.10e-14 ***
## glucose_jeun -0.009463 0.036407 -0.260 0.795
## age 0.061940 0.007554 8.200 2.40e-16 ***
## sexe 0.518029 0.132239 3.917 8.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1374.5 on 1058 degrees of freedom
## AIC: 1382.5
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0082118 0.0023842 0.0273 1.105e-14 ***
## glucose_jeun 0.9905821 0.9225289 1.0652 0.7949
## age 1.0638989 1.0484712 1.0800 2.400e-16 ***
## sexe 1.6787148 1.2965126 2.1777 8.952e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 3
Modèle
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0514 -1.0820 0.6300 0.9997 2.0329
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.298000 0.645185 -8.212 < 2e-16 ***
## glucose_jeun -0.018725 0.037419 -0.500 0.616782
## age 0.064048 0.007787 8.225 < 2e-16 ***
## sexe 0.508909 0.136188 3.737 0.000186 ***
## apoe.reg2 0.835986 0.109611 7.627 2.41e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1310.6 on 1057 degrees of freedom
## AIC: 1320.6
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0050016 0.0013833 0.0174 < 2.2e-16 ***
## glucose_jeun 0.9814492 0.9119392 1.0571 0.6167817
## age 1.0661432 1.0502179 1.0828 < 2.2e-16 ***
## sexe 1.6634760 1.2748621 2.1748 0.0001864 ***
## apoe.reg2 2.3070884 1.8666250 2.8695 2.405e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 4
Modèle
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0772 -1.0711 0.6329 0.9878 2.0364
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.387699 0.655519 -8.219 < 2e-16 ***
## glucose_jeun -0.017048 0.041537 -0.410 0.681484
## age 0.065620 0.007919 8.287 < 2e-16 ***
## sexe 0.510101 0.136270 3.743 0.000182 ***
## apoe.reg2 0.840498 0.109982 7.642 2.14e-14 ***
## ch -0.222286 0.188180 -1.181 0.237508
## dia 0.015966 0.273484 0.058 0.953445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1309.2 on 1055 degrees of freedom
## AIC: 1323.2
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0045725 0.0012383 0.0162 < 2.2e-16 ***
## glucose_jeun 0.9830963 0.9061876 1.0676 0.6814844
## age 1.0678209 1.0516056 1.0848 < 2.2e-16 ***
## sexe 1.6654596 1.2761794 2.1778 0.0001816 ***
## apoe.reg2 2.3175199 1.8736955 2.8845 2.136e-14 ***
## ch 0.8006864 0.5538763 1.1593 0.2375080
## dia 1.0160943 0.5943009 1.7412 0.9534454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 5
Modèle
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2921 -1.0301 0.5591 0.9620 2.0668
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.15206 0.74252 -4.245 2.19e-05 ***
## glucose_jeun -0.02655 0.04209 -0.631 0.52821
## age 0.06540 0.00812 8.054 8.03e-16 ***
## sexe 0.44616 0.13936 3.201 0.00137 **
## apoe.reg2 0.77796 0.11147 6.979 2.97e-12 ***
## ch -0.20651 0.19132 -1.079 0.28041
## dia 0.02985 0.27861 0.107 0.91468
## mmse -0.08995 0.01410 -6.377 1.80e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1265.1 on 1054 degrees of freedom
## AIC: 1281.1
##
## Number of Fisher Scoring iterations: 3Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0427641 0.0098501 0.1815 2.185e-05 ***
## glucose_jeun 0.9738012 0.8968111 1.0589 0.528212
## age 1.0675838 1.0509627 1.0850 8.033e-16 ***
## sexe 1.5623053 1.1896561 2.0550 0.001367 **
## apoe.reg2 2.1770350 1.7545592 2.7171 2.971e-12 ***
## ch 0.8134203 0.5592232 1.1850 0.280407
## dia 1.0302988 0.5968231 1.7841 0.914681
## mmse 0.9139769 0.8886699 0.9392 1.802e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 6
Modèle
##
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_tm_g)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3933 -0.9979 0.5032 0.9449 2.1521
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.058634 0.860332 -1.230 0.21851
## glucose_jeun 0.014078 0.044227 0.318 0.75024
## age 0.063439 0.008246 7.694 1.43e-14 ***
## sexe 0.415079 0.140989 2.944 0.00324 **
## apoe.reg2 0.734846 0.112953 6.506 7.73e-11 ***
## ch -0.137548 0.194421 -0.707 0.47927
## dia 0.067999 0.282772 0.240 0.80996
## mmse -0.092277 0.014268 -6.468 9.96e-11 ***
## imc -0.083022 0.017256 -4.811 1.50e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1459.5 on 1061 degrees of freedom
## Residual deviance: 1240.9 on 1053 degrees of freedom
## AIC: 1258.9
##
## Number of Fisher Scoring iterations: 3Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.346929 0.063946 1.8707 0.218512
## glucose_jeun 1.014178 0.930789 1.1078 0.750244
## age 1.065495 1.048645 1.0831 1.429e-14 ***
## sexe 1.514491 1.149463 1.9983 0.003239 **
## apoe.reg2 2.085161 1.675472 2.6098 7.729e-11 ***
## ch 0.871493 0.595617 1.2776 0.479272
## dia 1.070364 0.614933 1.8685 0.809963
## mmse 0.911853 0.886304 0.9373 9.959e-11 ***
## imc 0.920331 0.889359 0.9517 1.500e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Tableau récapitulatif
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| glucose_jeun | 0.96 | 0.90, 1.03 | 0.3 | 0.99 | 0.92, 1.07 | 0.8 | 0.98 | 0.91, 1.06 | 0.6 | 0.98 | 0.91, 1.07 | 0.7 | 0.97 | 0.90, 1.06 | 0.5 | 1.01 | 0.93, 1.11 | 0.8 |
| age | 1.06 | 1.05, 1.08 | <0.001 | 1.06 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 |
| sexe | 1.68 | 1.30, 2.18 | <0.001 | 1.66 | 1.27, 2.17 | <0.001 | 1.67 | 1.28, 2.18 | <0.001 | 1.56 | 1.19, 2.05 | 0.001 | 1.51 | 1.15, 2.00 | 0.003 | |||
| apoe.reg2 | 2.31 | 1.87, 2.87 | <0.001 | 2.32 | 1.87, 2.88 | <0.001 | 2.18 | 1.75, 2.72 | <0.001 | 2.09 | 1.68, 2.61 | <0.001 | ||||||
| ch | 0.80 | 0.55, 1.16 | 0.2 | 0.81 | 0.56, 1.18 | 0.3 | 0.87 | 0.60, 1.28 | 0.5 | |||||||||
| dia | 1.02 | 0.59, 1.74 | >0.9 | 1.03 | 0.60, 1.78 | >0.9 | 1.07 | 0.61, 1.87 | 0.8 | |||||||||
| mmse | 0.91 | 0.89, 0.94 | <0.001 | 0.91 | 0.89, 0.94 | <0.001 | ||||||||||||
| imc | 0.92 | 0.89, 0.95 | <0.001 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
Modèles sur la Triglycéride
Légende
## [1] "N = 962"Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE +
IMC
Modèle 1
Modèle
##
## Call:
## glm(formula = taut2 ~ triglycerides + age, family = binomial(logit),
## data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7970 -1.2131 0.7845 1.0419 1.7015
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.447982 0.574677 -6.000 1.97e-09 ***
## triglycerides -0.388536 0.189244 -2.053 0.0401 *
## age 0.058717 0.007851 7.479 7.50e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1253.0 on 959 degrees of freedom
## AIC: 1259
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.031810 0.010172 0.0970 1.975e-09 ***
## triglycerides 0.678049 0.467221 0.9818 0.04006 *
## age 1.060475 1.044485 1.0772 7.499e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 2
Modèle
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe, family = binomial(logit),
## data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.935 -1.178 0.752 1.021 1.807
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.568139 0.647516 -7.055 1.73e-12 ***
## triglycerides -0.339848 0.191594 -1.774 0.0761 .
## age 0.061632 0.007991 7.713 1.23e-14 ***
## sexe 0.567849 0.137217 4.138 3.50e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1235.6 on 958 degrees of freedom
## AIC: 1243.6
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0103773 0.0028633 0.0363 1.728e-12 ***
## triglycerides 0.7118782 0.4883272 1.0357 0.0761 .
## age 1.0635707 1.0472623 1.0806 1.231e-14 ***
## sexe 1.7644671 1.3496249 2.3117 3.499e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 3
Modèle
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0845 -1.0719 0.6276 0.9747 2.0244
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.142770 0.674758 -7.622 2.50e-14 ***
## triglycerides -0.367371 0.198484 -1.851 0.0642 .
## age 0.064078 0.008262 7.756 8.78e-15 ***
## sexe 0.561915 0.141717 3.965 7.34e-05 ***
## apoe.reg2 0.868569 0.116071 7.483 7.26e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1173.8 on 957 degrees of freedom
## AIC: 1183.8
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0058415 0.0015234 0.0215 2.505e-14 ***
## triglycerides 0.6925525 0.4684666 1.0208 0.06419 .
## age 1.0661754 1.0492939 1.0839 8.779e-15 ***
## sexe 1.7540276 1.3299148 2.3186 7.338e-05 ***
## apoe.reg2 2.3834970 1.9048714 3.0035 7.259e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 4
Modèle
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1091 -1.0677 0.6217 0.9671 2.0283
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.221243 0.679373 -7.685 1.53e-14 ***
## triglycerides -0.375945 0.199391 -1.885 0.0594 .
## age 0.065659 0.008409 7.808 5.79e-15 ***
## sexe 0.564857 0.142488 3.964 7.36e-05 ***
## apoe.reg2 0.870242 0.116061 7.498 6.47e-14 ***
## ch -0.238033 0.203516 -1.170 0.2422
## dia 0.082530 0.268912 0.307 0.7589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1172.5 on 955 degrees of freedom
## AIC: 1186.5
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0054006 0.0013953 0.0201 1.525e-14 ***
## triglycerides 0.6866401 0.4636211 1.0138 0.05937 .
## age 1.0678624 1.0506600 1.0859 5.790e-15 ***
## sexe 1.7591956 1.3318415 2.3290 7.363e-05 ***
## apoe.reg2 2.3874891 1.9080557 3.0084 6.472e-14 ***
## ch 0.7881769 0.5292142 1.1766 0.24216
## dia 1.0860317 0.6421532 1.8489 0.75892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 5
Modèle
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3800 -1.0147 0.5313 0.9458 2.1547
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.72841 0.77960 -3.500 0.000466 ***
## triglycerides -0.46211 0.20339 -2.272 0.023087 *
## age 0.06562 0.00867 7.569 3.77e-14 ***
## sexe 0.51551 0.14618 3.527 0.000421 ***
## apoe.reg2 0.78521 0.11790 6.660 2.74e-11 ***
## ch -0.23256 0.20783 -1.119 0.263152
## dia 0.08998 0.27700 0.325 0.745312
## mmse -0.10112 0.01528 -6.617 3.65e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1124.3 on 954 degrees of freedom
## AIC: 1140.3
##
## Number of Fisher Scoring iterations: 3Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.065323 0.014017 0.2987 0.0004656 ***
## triglycerides 0.629954 0.421984 0.9374 0.0230867 *
## age 1.067822 1.050093 1.0864 3.771e-14 ***
## sexe 1.674497 1.258364 2.2327 0.0004210 ***
## apoe.reg2 2.192862 1.745586 2.7724 2.740e-11 ***
## ch 0.792504 0.527599 1.1931 0.2631522
## dia 1.094149 0.636707 1.8922 0.7453116
## mmse 0.903824 0.876684 0.9309 3.655e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 6
Modèle
##
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_tm_t)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4733 -0.9876 0.4984 0.9145 2.2043
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.637392 0.903813 -0.705 0.48067
## triglycerides -0.322749 0.207873 -1.553 0.12051
## age 0.065273 0.008837 7.386 1.51e-13 ***
## sexe 0.465527 0.148107 3.143 0.00167 **
## apoe.reg2 0.750311 0.119546 6.276 3.47e-10 ***
## ch -0.171779 0.210855 -0.815 0.41526
## dia 0.220226 0.282967 0.778 0.43641
## mmse -0.102425 0.015413 -6.645 3.03e-11 ***
## imc -0.083872 0.018261 -4.593 4.37e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1318.1 on 961 degrees of freedom
## Residual deviance: 1102.3 on 953 degrees of freedom
## AIC: 1120.3
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.528669 0.089739 3.1149 0.480670
## triglycerides 0.724156 0.481135 1.0878 0.120513
## age 1.067450 1.049385 1.0864 1.511e-13 ***
## sexe 1.592853 1.192279 2.1315 0.001671 **
## apoe.reg2 2.117658 1.680238 2.6858 3.467e-10 ***
## ch 0.842165 0.557440 1.2755 0.415257
## dia 1.246358 0.717662 2.1832 0.436408
## mmse 0.902646 0.875307 0.9299 3.027e-11 ***
## imc 0.919549 0.886811 0.9527 4.371e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Tableau récapitulatif
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| triglycerides | 0.68 | 0.47, 0.98 | 0.040 | 0.71 | 0.49, 1.04 | 0.076 | 0.69 | 0.47, 1.02 | 0.064 | 0.69 | 0.46, 1.01 | 0.059 | 0.63 | 0.42, 0.94 | 0.023 | 0.72 | 0.48, 1.09 | 0.12 |
| age | 1.06 | 1.04, 1.08 | <0.001 | 1.06 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.09 | <0.001 | 1.07 | 1.05, 1.09 | <0.001 | 1.07 | 1.05, 1.09 | <0.001 |
| sexe | 1.76 | 1.35, 2.31 | <0.001 | 1.75 | 1.33, 2.32 | <0.001 | 1.76 | 1.33, 2.33 | <0.001 | 1.67 | 1.26, 2.23 | <0.001 | 1.59 | 1.19, 2.13 | 0.002 | |||
| apoe.reg2 | 2.38 | 1.90, 3.00 | <0.001 | 2.39 | 1.91, 3.01 | <0.001 | 2.19 | 1.75, 2.77 | <0.001 | 2.12 | 1.68, 2.69 | <0.001 | ||||||
| ch | 0.79 | 0.53, 1.18 | 0.2 | 0.79 | 0.53, 1.19 | 0.3 | 0.84 | 0.56, 1.28 | 0.4 | |||||||||
| dia | 1.09 | 0.64, 1.85 | 0.8 | 1.09 | 0.64, 1.89 | 0.7 | 1.25 | 0.72, 2.18 | 0.4 | |||||||||
| mmse | 0.90 | 0.88, 0.93 | <0.001 | 0.90 | 0.88, 0.93 | <0.001 | ||||||||||||
| imc | 0.92 | 0.89, 0.95 | <0.001 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
Modèles sur le Cholestérol
Légende
## [1] "N = 1006"Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE +
IMC
Modèle 1
Modèle
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age, family = binomial(logit),
## data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9232 -1.1860 0.7911 1.0419 1.6832
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.660714 0.647808 -7.195 6.27e-13 ***
## cholesterol_total 0.307506 0.133425 2.305 0.0212 *
## age 0.061145 0.007735 7.905 2.67e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1311.0 on 1003 degrees of freedom
## AIC: 1317
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0094597 0.0026040 0.0331 6.265e-13 ***
## cholesterol_total 1.3600295 1.0509789 1.7738 0.02118 *
## age 1.0630526 1.0472631 1.0795 2.671e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 2
Modèle
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe, family = binomial(logit),
## data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.915 -1.171 0.747 1.033 1.789
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.213730 0.670521 -7.776 7.51e-15 ***
## cholesterol_total 0.141583 0.139622 1.014 0.310559
## age 0.062791 0.007821 8.029 9.86e-16 ***
## sexe 0.520688 0.140957 3.694 0.000221 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1297.2 on 1002 degrees of freedom
## AIC: 1305.2
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0054413 0.0014307 0.0199 7.507e-15 ***
## cholesterol_total 1.1520963 0.8789710 1.5207 0.3105593
## age 1.0648038 1.0488198 1.0815 9.863e-16 ***
## sexe 1.6831846 1.2780364 2.2215 0.0002208 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 3
Modèle
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2,
## family = binomial(logit), data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0700 -1.0748 0.6220 0.9793 1.9914
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.649346 0.694910 -8.130 4.31e-16 ***
## cholesterol_total 0.049765 0.141842 0.351 0.725702
## age 0.065037 0.008087 8.042 8.81e-16 ***
## sexe 0.542792 0.145418 3.733 0.000189 ***
## apoe.reg2 0.875663 0.114193 7.668 1.74e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1232.3 on 1001 degrees of freedom
## AIC: 1242.3
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.00351982 0.00088062 0.0135 4.307e-16 ***
## cholesterol_total 1.05102417 0.79646116 1.3921 0.7257016
## age 1.06719872 1.05065308 1.0845 8.813e-16 ***
## sexe 1.72080548 1.29536480 2.2914 0.0001895 ***
## apoe.reg2 2.40046625 1.92522567 3.0133 1.743e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 4
Modèle
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia, family = binomial(logit), data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1020 -1.0681 0.6216 0.9718 1.9898
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.713135 0.699717 -8.165 3.22e-16 ***
## cholesterol_total 0.029640 0.143704 0.206 0.836590
## age 0.066972 0.008216 8.152 3.59e-16 ***
## sexe 0.549691 0.145812 3.770 0.000163 ***
## apoe.reg2 0.880076 0.114262 7.702 1.34e-14 ***
## ch -0.297787 0.196708 -1.514 0.130062
## dia 0.057587 0.259896 0.222 0.824642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1230.1 on 999 degrees of freedom
## AIC: 1244.1
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.00330230 0.00081814 0.0127 3.216e-16 ***
## cholesterol_total 1.03008375 0.77741091 1.3690 0.8365895
## age 1.06926569 1.05242906 1.0869 3.594e-16 ***
## sexe 1.73271711 1.30333006 2.3090 0.0001633 ***
## apoe.reg2 2.41108347 1.93345772 3.0270 1.337e-14 ***
## ch 0.74245915 0.50489384 1.0928 0.1300622
## dia 1.05927791 0.63691485 1.7694 0.8246417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 5
Modèle
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse, family = binomial(logit), data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3359 -1.0154 0.5441 0.9402 2.1084
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.495554 0.778888 -4.488 7.19e-06 ***
## cholesterol_total 0.074160 0.146956 0.505 0.61381
## age 0.067420 0.008462 7.968 1.61e-15 ***
## sexe 0.480575 0.149629 3.212 0.00132 **
## apoe.reg2 0.797070 0.115989 6.872 6.33e-12 ***
## ch -0.280416 0.200623 -1.398 0.16219
## dia 0.045243 0.266505 0.170 0.86520
## mmse -0.097057 0.014862 -6.531 6.55e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1183.5 on 998 degrees of freedom
## AIC: 1199.5
##
## Number of Fisher Scoring iterations: 3Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.0303320 0.0064902 0.1379 7.194e-06 ***
## cholesterol_total 1.0769794 0.8079718 1.4413 0.613811
## age 1.0697447 1.0524064 1.0879 1.615e-15 ***
## sexe 1.6170038 1.2068952 2.1705 0.001319 **
## apoe.reg2 2.2190299 1.7728729 2.7946 6.333e-12 ***
## ch 0.7554693 0.5098089 1.1205 0.162194
## dia 1.0462816 0.6210770 1.7706 0.865197
## mmse 0.9075046 0.8810193 0.9339 6.552e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Modèle 6
Modèle
##
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, family = binomial(logit), data = data_tm_c)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4409 -0.9879 0.4925 0.9248 2.1827
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.170054 0.927097 -1.262 0.20693
## cholesterol_total 0.014869 0.149807 0.099 0.92094
## age 0.066031 0.008611 7.668 1.74e-14 ***
## sexe 0.443433 0.151478 2.927 0.00342 **
## apoe.reg2 0.764500 0.117424 6.511 7.49e-11 ***
## ch -0.232096 0.203499 -1.141 0.25407
## dia 0.160569 0.271672 0.591 0.55449
## mmse -0.098491 0.015002 -6.565 5.19e-11 ***
## imc -0.080280 0.017480 -4.593 4.38e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1380.7 on 1005 degrees of freedom
## Residual deviance: 1161.5 on 997 degrees of freedom
## AIC: 1179.5
##
## Number of Fisher Scoring iterations: 4Odds ratio
## Attente de la réalisation du profilage...## OR 2.5 % 97.5 % p
## (Intercept) 0.310350 0.050153 1.9073 0.206927
## cholesterol_total 1.014980 0.757085 1.3655 0.920935
## age 1.068259 1.050633 1.0867 1.742e-14 ***
## sexe 1.558046 1.158526 2.0987 0.003418 **
## apoe.reg2 2.147921 1.711151 2.7125 7.485e-11 ***
## ch 0.792870 0.532107 1.1828 0.254067
## dia 1.174179 0.690688 2.0095 0.554493
## mmse 0.906204 0.879506 0.9328 5.189e-11 ***
## imc 0.922858 0.891390 0.9547 4.377e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Tableau récapitulatif
| Characteristic | Modèle 1 | Modèle 2 | Modèle 3 | Modèle 4 | Modèle 5 | Modèle 6 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| glucose_jeun | 0.96 | 0.90, 1.03 | 0.3 | 0.99 | 0.92, 1.07 | 0.8 | 0.98 | 0.91, 1.06 | 0.6 | 0.98 | 0.91, 1.07 | 0.7 | 0.97 | 0.90, 1.06 | 0.5 | 1.01 | 0.93, 1.11 | 0.8 |
| age | 1.06 | 1.05, 1.08 | <0.001 | 1.06 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 | 1.07 | 1.05, 1.08 | <0.001 |
| sexe | 1.68 | 1.30, 2.18 | <0.001 | 1.66 | 1.27, 2.17 | <0.001 | 1.67 | 1.28, 2.18 | <0.001 | 1.56 | 1.19, 2.05 | 0.001 | 1.51 | 1.15, 2.00 | 0.003 | |||
| apoe.reg2 | 2.31 | 1.87, 2.87 | <0.001 | 2.32 | 1.87, 2.88 | <0.001 | 2.18 | 1.75, 2.72 | <0.001 | 2.09 | 1.68, 2.61 | <0.001 | ||||||
| ch | 0.80 | 0.55, 1.16 | 0.2 | 0.81 | 0.56, 1.18 | 0.3 | 0.87 | 0.60, 1.28 | 0.5 | |||||||||
| dia | 1.02 | 0.59, 1.74 | >0.9 | 1.03 | 0.60, 1.78 | >0.9 | 1.07 | 0.61, 1.87 | 0.8 | |||||||||
| mmse | 0.91 | 0.89, 0.94 | <0.001 | 0.91 | 0.89, 0.94 | <0.001 | ||||||||||||
| imc | 0.92 | 0.89, 0.95 | <0.001 | |||||||||||||||
| 1 OR = Odds Ratio, CI = Confidence Interval | ||||||||||||||||||
Tau en continu
Nombre d’Observations dans les modèles
## [1] "Modèle"## [1] " + tau_"## [1] "N = 1722"## [1] " + glucose_jeun"## [1] "N = 1501"## [1] " + triglycerides"## [1] "N = 1281"## [1] " + cholesterol_total"## [1] "N = 1280"## [1] " + age"## [1] "N = 1280"## [1] " + sexe"## [1] "N = 1280"## [1] " + apoe.reg2"## [1] "N = 1176"## [1] " + ch"## [1] "N = 1176"## [1] " + mmse"## [1] "N = 1098"## [1] " + dia"## [1] "N = 1098"## [1] " + imc"## [1] "N = 934"Matrice de corrélation
Modèles sur le Glucose
Légende
## [1] "N = 1061"Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC
Modèle 1
Modèle
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3354 -0.6850 -0.2529 0.4050 3.8102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.358451 0.237168 -1.511 0.1310
## glucose_jeun -0.013720 0.016476 -0.833 0.4052
## age 0.006052 0.003224 1.877 0.0608 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9501 on 1058 degrees of freedom
## Multiple R-squared: 0.003741, Adjusted R-squared: 0.001858
## F-statistic: 1.987 on 2 and 1058 DF, p-value: 0.1377Scatter plot
Régression linéaire
## Warning in abline(tau_glucose.mod, col = "red"): utilisation des deux premiers
## des 3 coefficients de régression
Modèle 2
Modèle
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3861 -0.6907 -0.2338 0.4097 3.8587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.607614 0.266579 -2.279 0.0228 *
## glucose_jeun -0.007215 0.016759 -0.431 0.6669
## age 0.006424 0.003224 1.992 0.0466 *
## sexe 0.121337 0.059607 2.036 0.0420 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9487 on 1057 degrees of freedom
## Multiple R-squared: 0.007632, Adjusted R-squared: 0.004815
## F-statistic: 2.71 on 3 and 1057 DF, p-value: 0.04396Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
Modèle 3
Modèle
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4206 -0.6885 -0.2479 0.4240 3.8212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.631759 0.266775 -2.368 0.0181 *
## glucose_jeun -0.007835 0.016750 -0.468 0.6400
## age 0.006343 0.003222 1.968 0.0493 *
## sexe 0.118110 0.059592 1.982 0.0477 *
## apoe.reg2 0.073792 0.045081 1.637 0.1020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9479 on 1056 degrees of freedom
## Multiple R-squared: 0.01014, Adjusted R-squared: 0.006394
## F-statistic: 2.705 on 4 and 1056 DF, p-value: 0.0292Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
Modèle 4
Modèle
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch +
## dia, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4198 -0.6862 -0.2462 0.4220 3.8228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.621889 0.270537 -2.299 0.0217 *
## glucose_jeun -0.009594 0.018346 -0.523 0.6011
## age 0.006298 0.003271 1.925 0.0544 .
## sexe 0.118187 0.059648 1.981 0.0478 *
## apoe.reg2 0.074488 0.045313 1.644 0.1005
## ch 0.005433 0.082812 0.066 0.9477
## dia 0.027684 0.122759 0.226 0.8216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9488 on 1054 degrees of freedom
## Multiple R-squared: 0.0102, Adjusted R-squared: 0.004566
## F-statistic: 1.81 on 6 and 1054 DF, p-value: 0.09388Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
Modèle 5
Modèle
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch +
## dia + mmse, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3997 -0.6782 -0.2538 0.4075 3.7472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.241859 0.310663 -0.779 0.4364
## glucose_jeun -0.010997 0.018310 -0.601 0.5482
## age 0.005833 0.003268 1.785 0.0746 .
## sexe 0.105672 0.059719 1.769 0.0771 .
## apoe.reg2 0.059700 0.045598 1.309 0.1907
## ch 0.011314 0.082647 0.137 0.8911
## dia 0.030215 0.122467 0.247 0.8052
## mmse -0.013959 0.005651 -2.470 0.0137 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9465 on 1053 degrees of freedom
## Multiple R-squared: 0.0159, Adjusted R-squared: 0.00936
## F-statistic: 2.431 on 7 and 1053 DF, p-value: 0.01793Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
Modèle 6
Modèle
##
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch +
## dia + mmse + imc, data = data_tc_g)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4564 -0.6647 -0.2420 0.4117 3.7247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.213477 0.362795 0.588 0.5564
## glucose_jeun -0.001976 0.018646 -0.106 0.9156
## age 0.005037 0.003278 1.537 0.1247
## sexe 0.096553 0.059702 1.617 0.1061
## apoe.reg2 0.048024 0.045750 1.050 0.2941
## ch 0.024566 0.082640 0.297 0.7663
## dia 0.041281 0.122273 0.338 0.7357
## mmse -0.014116 0.005639 -2.503 0.0125 *
## imc -0.017243 0.007140 -2.415 0.0159 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9443 on 1052 degrees of freedom
## Multiple R-squared: 0.02133, Adjusted R-squared: 0.01388
## F-statistic: 2.866 on 8 and 1052 DF, p-value: 0.003709Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
Tableau récapitulatif
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | -0.02 | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 |
| [-0.08, 0.04],(0.54) | [-0.17, 0.00],(0.06) | [-0.16, 0.00],(0.06) | [-0.17, 0.01],(0.07) | [-0.17, 0.01],(0.09) | [-0.16, 0.01],(0.09) | |
| glucose_jeun | -0.02 | -0.01 | -0.01 | -0.02 | -0.02 | -0.00 |
| [-0.08, 0.03],(0.41) | [-0.07, 0.05],(0.67) | [-0.07, 0.04],(0.64) | [-0.08, 0.05],(0.60) | [-0.08, 0.04],(0.55) | [-0.07, 0.06],(0.92) | |
| age | 0.05 | 0.06 * | 0.06 * | 0.06 | 0.05 | 0.05 |
| [-0.00, 0.11],(0.06) | [0.00, 0.12],(0.05) | [0.00, 0.12],(0.05) | [-0.00, 0.12],(0.05) | [-0.01, 0.11],(0.07) | [-0.01, 0.10],(0.12) | |
| sexe | 0.12 * | 0.12 * | 0.12 * | 0.11 | 0.10 | |
| [0.00, 0.24],(0.04) | [0.00, 0.24],(0.05) | [0.00, 0.24],(0.05) | [-0.01, 0.22],(0.08) | [-0.02, 0.21],(0.11) | ||
| apoe.reg2 | 0.05 | 0.05 | 0.04 | 0.03 | ||
| [-0.01, 0.10],(0.10) | [-0.01, 0.11],(0.10) | [-0.02, 0.10],(0.19) | [-0.03, 0.09],(0.29) | |||
| ch | 0.01 | 0.01 | 0.02 | |||
| [-0.16, 0.17],(0.95) | [-0.15, 0.17],(0.89) | [-0.14, 0.19],(0.77) | ||||
| dia | 0.03 | 0.03 | 0.04 | |||
| [-0.21, 0.27],(0.82) | [-0.21, 0.27],(0.81) | [-0.20, 0.28],(0.74) | ||||
| mmse | -0.07 * | -0.07 * | ||||
| [-0.13, -0.01],(0.01) | [-0.13, -0.02],(0.01) | |||||
| imc | -0.07 * | |||||
| [-0.13, -0.01],(0.02) | ||||||
| N | 1061 | 1061 | 1061 | 1061 | 1061 | 1061 |
| R2 | 0.00 | 0.01 | 0.01 | 0.01 | 0.02 | 0.02 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
Modèles sur la Triglycéride
Légende
## [1] "N = 961"Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE +
IMC
Modèle 1
Modèle
##
## Call:
## lm(formula = tau_ ~ triglycerides + age, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3294 -0.6933 -0.2524 0.4246 3.8024
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.290063 0.256226 -1.132 0.258
## triglycerides -0.011627 0.086562 -0.134 0.893
## age 0.004173 0.003430 1.217 0.224
##
## Residual standard error: 0.9543 on 958 degrees of freedom
## Multiple R-squared: 0.001567, Adjusted R-squared: -0.000517
## F-statistic: 0.752 on 2 and 958 DF, p-value: 0.4717Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression
Modèle 2
Modèle
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3825 -0.6983 -0.2462 0.4234 3.8588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5121989 0.2800396 -1.829 0.0677 .
## triglycerides 0.0001375 0.0866452 0.002 0.9987
## age 0.0045282 0.0034296 1.320 0.1870
## sexe 0.1207923 0.0619095 1.951 0.0513 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9529 on 957 degrees of freedom
## Multiple R-squared: 0.005523, Adjusted R-squared: 0.002406
## F-statistic: 1.772 on 3 and 957 DF, p-value: 0.1509Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
Modèle 3
Modèle
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4180 -0.6987 -0.2488 0.4374 3.8198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5425169 0.2803782 -1.935 0.0533 .
## triglycerides -0.0005452 0.0865668 -0.006 0.9950
## age 0.0044611 0.0034267 1.302 0.1933
## sexe 0.1168435 0.0618985 1.888 0.0594 .
## apoe.reg2 0.0789522 0.0475524 1.660 0.0972 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.952 on 956 degrees of freedom
## Multiple R-squared: 0.008383, Adjusted R-squared: 0.004234
## F-statistic: 2.02 on 4 and 956 DF, p-value: 0.08955Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
Modèle 4
Modèle
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4195 -0.6996 -0.2475 0.4318 3.8215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.547546 0.281860 -1.943 0.0524 .
## triglycerides -0.003013 0.086965 -0.035 0.9724
## age 0.004521 0.003480 1.299 0.1942
## sexe 0.118480 0.062210 1.905 0.0571 .
## apoe.reg2 0.079812 0.047669 1.674 0.0944 .
## ch -0.017019 0.089410 -0.190 0.8491
## dia 0.038308 0.122015 0.314 0.7536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.953 on 954 degrees of freedom
## Multiple R-squared: 0.008506, Adjusted R-squared: 0.00227
## F-statistic: 1.364 on 6 and 954 DF, p-value: 0.2262Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
Modèle 5
Modèle
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4008 -0.6864 -0.2551 0.4471 3.7573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.198120 0.324303 -0.611 0.5414
## triglycerides -0.010103 0.086859 -0.116 0.9074
## age 0.004100 0.003479 1.178 0.2389
## sexe 0.109213 0.062237 1.755 0.0796 .
## apoe.reg2 0.063762 0.048150 1.324 0.1857
## ch -0.012565 0.089261 -0.141 0.8881
## dia 0.038073 0.121780 0.313 0.7546
## mmse -0.012989 0.005998 -2.166 0.0306 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9511 on 953 degrees of freedom
## Multiple R-squared: 0.01336, Adjusted R-squared: 0.006114
## F-statistic: 1.844 on 7 and 953 DF, p-value: 0.07578Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
Modèle 6
Modèle
##
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_tc_t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4449 -0.6810 -0.2503 0.4319 3.7275
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.172897 0.381455 0.453 0.6505
## triglycerides 0.013891 0.087724 0.158 0.8742
## age 0.003630 0.003484 1.042 0.2978
## sexe 0.098761 0.062418 1.582 0.1139
## apoe.reg2 0.055450 0.048302 1.148 0.2513
## ch -0.002872 0.089305 -0.032 0.9743
## dia 0.063978 0.122439 0.523 0.6014
## mmse -0.012965 0.005990 -2.164 0.0307 *
## imc -0.013847 0.007520 -1.841 0.0659 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9499 on 952 degrees of freedom
## Multiple R-squared: 0.01686, Adjusted R-squared: 0.008601
## F-statistic: 2.041 on 8 and 952 DF, p-value: 0.03906Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
Tableau récapitulatif
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | -0.01 | -0.08 | -0.07 | -0.07 | -0.07 | -0.08 |
| [-0.07, 0.05],(0.73) | [-0.16, 0.01],(0.10) | [-0.16, 0.02],(0.10) | [-0.17, 0.02],(0.12) | [-0.17, 0.02],(0.14) | [-0.16, 0.01],(0.09) | |
| triglycerides | -0.00 | 0.00 | -0.00 | -0.00 | -0.00 | |
| [-0.06, 0.06],(0.89) | [-0.06, 0.06],(1.00) | [-0.06, 0.06],(0.99) | [-0.06, 0.06],(0.97) | [-0.06, 0.06],(0.91) | ||
| age | 0.04 | 0.04 | 0.04 | 0.04 | 0.04 | 0.05 |
| [-0.02, 0.10],(0.22) | [-0.02, 0.10],(0.19) | [-0.02, 0.10],(0.19) | [-0.02, 0.10],(0.19) | [-0.02, 0.10],(0.24) | [-0.01, 0.10],(0.12) | |
| sexe | 0.12 | 0.12 | 0.12 | 0.11 | 0.10 | |
| [-0.00, 0.24],(0.05) | [-0.00, 0.24],(0.06) | [-0.00, 0.24],(0.06) | [-0.01, 0.23],(0.08) | [-0.02, 0.21],(0.11) | ||
| apoe.reg2 | 0.05 | 0.05 | 0.04 | 0.03 | ||
| [-0.01, 0.11],(0.10) | [-0.01, 0.11],(0.09) | [-0.02, 0.10],(0.19) | [-0.03, 0.09],(0.29) | |||
| ch | -0.02 | -0.01 | 0.02 | |||
| [-0.19, 0.16],(0.85) | [-0.19, 0.16],(0.89) | [-0.14, 0.19],(0.77) | ||||
| dia | 0.04 | 0.04 | 0.04 | |||
| [-0.20, 0.28],(0.75) | [-0.20, 0.28],(0.75) | [-0.20, 0.28],(0.74) | ||||
| mmse | -0.07 * | -0.07 * | ||||
| [-0.13, -0.01],(0.03) | [-0.13, -0.02],(0.01) | |||||
| glucose_jeun | -0.00 | |||||
| [-0.07, 0.06],(0.92) | ||||||
| imc | -0.07 * | |||||
| [-0.13, -0.01],(0.02) | ||||||
| N | 961 | 961 | 961 | 961 | 961 | 1061 |
| R2 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 | 0.02 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
Modèles sur la Cholestérol
Légende
## [1] "N = 1005"Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE +
IMC
Modèle 1
Modèle
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3196 -0.6846 -0.2534 0.4241 3.8190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.236187 0.278097 -0.849 0.396
## cholesterol_total -0.018758 0.058313 -0.322 0.748
## age 0.003721 0.003337 1.115 0.265
##
## Residual standard error: 0.947 on 1002 degrees of freedom
## Multiple R-squared: 0.001451, Adjusted R-squared: -0.0005425
## F-statistic: 0.7278 on 2 and 1002 DF, p-value: 0.4832Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression
Modèle 2
Modèle
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3554 -0.6970 -0.2285 0.4346 3.8958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.367296 0.284120 -1.293 0.1964
## cholesterol_total -0.061565 0.061475 -1.001 0.3168
## age 0.003911 0.003332 1.174 0.2408
## sexe 0.136846 0.063229 2.164 0.0307 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9452 on 1001 degrees of freedom
## Multiple R-squared: 0.006101, Adjusted R-squared: 0.003123
## F-statistic: 2.048 on 3 and 1001 DF, p-value: 0.1055Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression
Modèle 3
Modèle
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2,
## data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3842 -0.6914 -0.2438 0.4463 3.8587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.381137 0.283880 -1.343 0.1797
## cholesterol_total -0.072091 0.061667 -1.169 0.2427
## age 0.003803 0.003329 1.143 0.2535
## sexe 0.136012 0.063155 2.154 0.0315 *
## apoe.reg2 0.085714 0.046494 1.844 0.0655 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9441 on 1000 degrees of freedom
## Multiple R-squared: 0.009468, Adjusted R-squared: 0.005506
## F-statistic: 2.39 on 4 and 1000 DF, p-value: 0.04929Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression
Modèle 4
Modèle
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3864 -0.6918 -0.2481 0.4432 3.8565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.383799 0.285389 -1.345 0.1790
## cholesterol_total -0.073568 0.062331 -1.180 0.2382
## age 0.003917 0.003372 1.162 0.2457
## sexe 0.136312 0.063358 2.151 0.0317 *
## apoe.reg2 0.086003 0.046615 1.845 0.0653 .
## ch -0.018846 0.086090 -0.219 0.8268
## dia 0.001231 0.116767 0.011 0.9916
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.945 on 998 degrees of freedom
## Multiple R-squared: 0.009516, Adjusted R-squared: 0.003561
## F-statistic: 1.598 on 6 and 998 DF, p-value: 0.1444Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression
Modèle 5
Modèle
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3690 -0.6807 -0.2439 0.4400 3.7838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0527127 0.3205715 -0.164 0.8694
## cholesterol_total -0.0653630 0.0623110 -1.049 0.2944
## age 0.0035516 0.0033695 1.054 0.2921
## sexe 0.1238271 0.0634726 1.951 0.0514 .
## apoe.reg2 0.0694395 0.0470989 1.474 0.1407
## ch -0.0124093 0.0859627 -0.144 0.8852
## dia 0.0001498 0.1165310 0.001 0.9990
## mmse -0.0131953 0.0058641 -2.250 0.0247 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9431 on 997 degrees of freedom
## Multiple R-squared: 0.01452, Adjusted R-squared: 0.007602
## F-statistic: 2.099 on 7 and 997 DF, p-value: 0.04119Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression
Modèle 6
Modèle
##
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 +
## ch + dia + mmse + imc, data = data_tc_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4160 -0.6762 -0.2356 0.4233 3.7649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.380934 0.388242 0.981 0.3267
## cholesterol_total -0.074902 0.062408 -1.200 0.2303
## age 0.002891 0.003381 0.855 0.3927
## sexe 0.114359 0.063562 1.799 0.0723 .
## apoe.reg2 0.061217 0.047215 1.297 0.1951
## ch -0.003842 0.085948 -0.045 0.9644
## dia 0.026347 0.117116 0.225 0.8221
## mmse -0.013154 0.005856 -2.246 0.0249 *
## imc -0.014106 0.007146 -1.974 0.0487 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9417 on 996 degrees of freedom
## Multiple R-squared: 0.01836, Adjusted R-squared: 0.01048
## F-statistic: 2.329 on 8 and 996 DF, p-value: 0.01769Scatter plot
Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression
Tableau récapitulatif
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | -0.02 | -0.09 * | -0.09 * | -0.09 | -0.08 | -0.08 |
| [-0.08, 0.04],(0.56) | [-0.18, -0.00],(0.05) | [-0.18, -0.00],(0.05) | [-0.18, 0.01],(0.07) | [-0.17, 0.01],(0.09) | [-0.17, 0.01],(0.09) | |
| cholesterol_total | -0.01 | -0.03 | -0.04 | -0.04 | -0.03 | -0.04 |
| [-0.07, 0.05],(0.75) | [-0.09, 0.03],(0.32) | [-0.10, 0.03],(0.24) | [-0.10, 0.03],(0.24) | [-0.10, 0.03],(0.29) | [-0.10, 0.02],(0.23) | |
| age | 0.03 | 0.04 | 0.03 | 0.04 | 0.03 | 0.03 |
| [-0.03, 0.09],(0.27) | [-0.02, 0.09],(0.24) | [-0.02, 0.09],(0.25) | [-0.02, 0.10],(0.25) | [-0.03, 0.09],(0.29) | [-0.03, 0.09],(0.39) | |
| sexe | 0.14 * | 0.14 * | 0.14 * | 0.12 | 0.11 | |
| [0.01, 0.26],(0.03) | [0.01, 0.26],(0.03) | [0.01, 0.26],(0.03) | [-0.00, 0.25],(0.05) | [-0.01, 0.24],(0.07) | ||
| apoe.reg2 | 0.06 | 0.06 | 0.04 | 0.04 | ||
| [-0.00, 0.11],(0.07) | [-0.00, 0.11],(0.07) | [-0.01, 0.10],(0.14) | [-0.02, 0.10],(0.20) | |||
| ch | -0.02 | -0.01 | -0.00 | |||
| [-0.19, 0.15],(0.83) | [-0.18, 0.16],(0.89) | [-0.17, 0.16],(0.96) | ||||
| dia | 0.00 | 0.00 | 0.03 | |||
| [-0.23, 0.23],(0.99) | [-0.23, 0.23],(1.00) | [-0.20, 0.26],(0.82) | ||||
| mmse | -0.07 * | -0.07 * | ||||
| [-0.13, -0.01],(0.02) | [-0.13, -0.01],(0.02) | |||||
| imc | -0.06 * | |||||
| [-0.12, -0.00],(0.05) | ||||||
| N | 1005 | 1005 | 1005 | 1005 | 1005 | 1005 |
| R2 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 | 0.02 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
# Export BDD avec scale variable
"data_export = data.frame(abeta42_continu = data2$abeta42_,
abeta_42_modalite = data2$abeta42t2,
tau_continu = data2$tau_,
tau_modalite = data2$taut2,
glucose = data2$glucose_jeun,
glucose_std = scale(data2$glucose_jeun),
triglycerides = data2$triglycerides,
triglycerides_std = scale(data2$triglycerides),
cholesterol_total = data2$cholesterol_total,
cholesterol_total_std = scale(data2$cholesterol_total),
cholesterol_hdl = data2$cholesterol_hdl,
cholesterol_hdl.std = scale(data2$cholesterol_hdl),
cholesterol_ldl = data2$cholesterol_ldl,
cholesterol_ldl.std = scale(data2$cholesterol_ldl),
education = data2$niveau_etude_reg,
age = data2$age,
sexe = data2$sexe_reg,
apoe = data2$apoe,
traitement_chol = data2$ch,
traitement_dia = data2$dia,
mmse = data2$mmse,
imc = data2$imc
)"## [1] "data_export = data.frame(abeta42_continu = data2$abeta42_,\n abeta_42_modalite = data2$abeta42t2,\n tau_continu = data2$tau_,\n tau_modalite = data2$taut2,\n glucose = data2$glucose_jeun,\n glucose_std = scale(data2$glucose_jeun),\n triglycerides = data2$triglycerides,\n triglycerides_std = scale(data2$triglycerides),\n cholesterol_total = data2$cholesterol_total,\n cholesterol_total_std = scale(data2$cholesterol_total),\n cholesterol_hdl = data2$cholesterol_hdl,\n cholesterol_hdl.std = scale(data2$cholesterol_hdl),\n cholesterol_ldl = data2$cholesterol_ldl,\n cholesterol_ldl.std = scale(data2$cholesterol_ldl),\n education = data2$niveau_etude_reg,\n age = data2$age,\n sexe = data2$sexe_reg,\n apoe = data2$apoe,\n traitement_chol = data2$ch,\n traitement_dia = data2$dia,\n mmse = data2$mmse,\n imc = data2$imc\n )"#write.csv(data_export,"//172.27.137.244/g_boilay/alternance/export/data_export.csv")